What does 'index 0 is out of bounds for axis 0 with size 0' mean?

Question

I am new to both python and numpy. I ran a code that I wrote and I am getting this message: 'index 0 is out of bounds for axis 0 with size 0' Without the context, I just want to figure out what this means.. It might be silly to ask this but what do they mean by axis 0 and size 0? index 0 means the first value in the array.. but I can't figure out what axis 0 and size 0 mean.

The 'data' is a text file with lots of numbers in two columns.

x = np.linspace(1735.0,1775.0,100)
column1 = (data[0,0:-1]+data[0,1:])/2.0
column2 = data[1,1:]
x_column1 = np.zeros(x.size+2)
x_column1[1:-1] = x
x_column1[0] = x[0]+x[0]-x[1]
x_column1[-1] = x[-1]+x[-1]-x[-2]
experiment = np.zeros_like(x)
for i in range(np.size(x_edges)-2):
    indexes = np.flatnonzero(np.logical_and((column1>=x_column1[i]),(column1<x_column1[i+1])))
    temp_column2 = column2[indexes]
    temp_column2[0] -= column2[indexes[0]]*(x_column1[i]-column1[indexes[0]-1])/(column1[indexes[0]]-column1[indexes[0]-1])
    temp_column2[-1] -= column2[indexes[-1]]*(column1[indexes[-1]+1]-x_column1[i+1])/(column1[indexes[-1]+1]-column1[indexes[-1]])
    experiment[i] = np.sum(temp_column2)   
return experiment

Answer 1

In numpy, index and dimension numbering starts with 0. So axis 0 means the 1st dimension. Also in numpy a dimension can have length (size) 0. The simplest case is:

In [435]: x = np.zeros((0,), int)
In [436]: x
Out[436]: array([], dtype=int32)
In [437]: x[0]
...
IndexError: index 0 is out of bounds for axis 0 with size 0

I also get it if x = np.zeros((0,5), int), a 2d array with 0 rows, and 5 columns.

So someplace in your code you are creating an array with a size 0 first axis.

When asking about errors, it is expected that you tell us where the error occurs.

Also when debugging problems like this, the first thing you should do is print the shape (and maybe the dtype) of the suspected variables.

Answer 2

Essentially it means you don't have the index you are trying to reference. For example:

df = pd.DataFrame()
df['this']=np.nan
df['my']=np.nan
df['data']=np.nan
df['data'][0]=5 #I haven't yet assigned how long df[data] should be!
print(df)

will give me the error you are referring to, because I haven't told Pandas how long my dataframe is. Whereas if I do the exact same code but I DO assign an index length, I don't get an error:

df = pd.DataFrame(index=[0,1,2,3,4])
df['this']=np.nan
df['is']=np.nan
df['my']=np.nan
df['data']=np.nan
df['data'][0]=5 #since I've properly labelled my index, I don't run into this problem!
print(df)

Hope that answers your question!